5514.0.55.001 - Australian System of Government Finance Statistics: Concepts, Sources and Methods, 2003
ARCHIVED ISSUE Released at 11:30 AM (CANBERRA TIME) 10/10/2003
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6.4. There are a number of factors which affect the accuracy and reliability of ABS government finance statistics. These include:
These factors are discussed below.
6.5. The quality of output is influenced by the nature of the source data available during the different phases of the GFS statistical cycle. The use of different ‘versions’ of annual source data (forward estimates, preliminary estimates, final data) and quarterly data affects the quality of output at each stage. The factors affecting quality of data at each stage are discussed in the following paragraphs.
6.6. Forward estimates are derived as part of government budget formulation processes. Because the data are based upon expectations relating to government policies (and the measures by which they will be funded), the accuracy of the data is subject to the course taken by subsequent events. For example, unforeseen trends in the economy could mean that levels of government expenditure, revenue and financing will run at higher or lower levels than anticipated during the budget year.
6.7. In general, because governments have direct control over much of their expenditure, they can anticipate final expenditure outcomes fairly well. While governments set revenue targets for the budget year, the extent of control they have over final revenue outcomes is not as strong as that for expenditures. For example, the level of major revenue items such as taxation depends upon the level of economic activity, which is not under the direct control of governments.
6.8. Furthermore, the forward GFS data are not as complete or as detailed as final annual data. This lack of detail in the forward data means that errors and omissions are less likely to be detected.
6.9. It should be noted that the forward estimates are not statistical projections or extrapolations generated by the ABS. The estimates are made by government budget offices based on planned or anticipated government policies. In a small number of cases the ABS may have to rely on estimates reported by individual entities. In a smaller number of cases where the supply of estimates is delayed, the ABS may use its own indicative estimates rather than jeopardise publication deadlines.
6.10. Preliminary estimates are, like the forward estimates, a product of government budget formulation processes. However, as the data are based upon actual outcomes, they are far more accurate and reliable. Of necessity, the estimates are compiled quickly and may therefore be revised when final audited data become available.
6.11. In the ABS GFS cycle, the preliminary estimates provide the first check of the reliability of the forward estimates and replace those estimates in the GFS system.
6.12. Final data are the complete audited data for any jurisdiction for any given year, and replace the preliminary data for that year. These data generally satisfy the level of detail required. However, some dissections required for national accounting purposes are not normally available in financial statements and audited accounts and these have to be estimated. For example, State-level estimates of Commonwealth Government final consumption expenditure, personal benefit payments and gross fixed capital formation are derived for publication in Australian National Accounts: State Accounts (ABS Cat. no. 5242.0). The estimates are made using distributive factors that are based on data series which vary in terms of quality and timeliness.
6.13. The accuracy and reliability of quarterly data are affected by the use of a degree of sampling in their compilation (see chapter 4). Consequently, quarterly data include a higher proportion of estimated data than preliminary and final data (see ‘Estimation errors’ below). They are also subject to revision when benchmarks are revised (see ‘Revisions’ below).
DATA COLLECTION TIMETABLES
6.14. Timetables for the collection and processing of GFS quarterly and annual data are necessarily very tight because users require the data as input to their own time-constrained programs. Quarterly production target dates are set mainly to meet the quarterly national accounts timetable, which requires the supply of quarterly GFS data six weeks after the end of the reference period. Users need the forward and preliminary estimates published in Government Finance Estimates, Australia (ABS Cat. no. 5501.0) to be available as soon as possible after governments’ budgets have been brought down (in particular, the usefulness and relevance of the forward estimates diminishes quickly after the budget year begins).
6.15. These deadlines affect the accuracy and reliability of GFS through their impact on:
6.16. While some of these processes can be carried out concurrently, only a limited amount of time can be allocated in total to all the tasks involved in order to meet fixed deadlines, so trade-offs between accuracy and timeliness have to be made.
6.17. Timeliness of GFS output differs for the different streams of data. Forward and quarterly estimates are the most timely, followed by preliminary data. The final data are the least timely GFS output.
6.18. As noted in chapter 2, not all in-scope enterprises are individually covered in GFS because the cost of collecting data from small units outweighs gains in accuracy and reliability. The way in which individual units are covered in GFS dictates the level of data estimation, which affects the quality of GFS. Most units are ‘directly’ covered while other units are ‘indirectly’ covered. A directly covered unit is one for which data from the unit’s accounts are included in GFS. An indirectly covered unit is one for which economic flows and stocks are deduced from data recorded by the directly covered units with which the indirectly covered unit undertakes transactions.
6.19. Indirect coverage of units is employed where the data of individual units are not readily available, are not available in sufficient time or are of insufficient statistical significance to warrant the cost of direct coverage. The most common example of units which are indirectly covered are public hospitals. Most of the data for the public hospitals in each State and Territory can be deduced from data in the records of the relevant jurisdiction’s health department.
6.20. While the detrimental impact of the indirect (partial) coverage of in-scope units on the accuracy and reliability of GFS has not been quantified, the amount of information missed by use of the procedure is judged to be small.
6.21. A small number of in-scope units are deliberately excluded from coverage because the cost of their inclusion outweighs the marginal increase in the accuracy of GFS. No statistical expansion is made to account for this undercoverage.
6.22. Use of sampling in some parts of GFS introduces sampling error as an element affecting the accuracy of the statistics. However, except for the minor exclusions identified under ‘Coverage’ above, the final annual, preliminary and forward estimates data attempt to cover all aspects of government operation. The quarterly data are compiled using a mix of full enumeration of larger units and some sampling of smaller units. Non-probability samples of local government authorities are used to produce quarterly estimates for the local government sector. As well, some dissections of quarterly data for other levels of government are estimated using previously recorded ratios. Overall, the use of sampling in Australia’s GFS is relatively minor and mainly affects the quarterly data.
6.23. Estimation errors for individual levels of government arising from the adjustments made for undercoverage built into the quarterly collection cannot be quantified readily. The estimation techniques involve assuming that the relationships between the collected and uncollected data that existed in the last annual benchmark census remain the same in the current quarter. The estimates made represent only a very small proportion of the value recorded for the data items concerned.
DATA PROCESSING ERRORS
6.24. The ABS GFS processing system has been designed to incorporate a series of data checks and edits (see chapter 4) with the purpose of minimising or eliminating data processing errors. However, data processing errors can go undetected either because there is insufficient time to undertake all the checks and edits, or because there is not a check or edit covering a particular error. Such occurrences affect the accuracy and reliability of GFS output. Undetected errors arising from incomplete editing are part of the cost of trading accuracy for timeliness. The errors in question are usually small and are usually detected when more complete editing can be undertaken. Errors that are not detected by input editing may be detected in output editing, which is an essential complement to the input editing process.
6.25. Errors may occur when a data supplier either provides an incorrect figure, or has to provide an estimate for data that are not readily available from accounting records. Errors can also occur because analysts may misclassify transactions in such a way that the errors are not detected in the editing process.
6.26. It is impossible to quantify the effect of undetected data processing errors. However, the effect of such errors that go undetected for a time but are eventually detected is reflected in revisions, which are quantifiable (see discussion ahead under ‘Assessment of accuracy, reliability and timeliness’).
6.27. Inaccuracies and imbalances may arise during the process of consolidating data. Inaccuracies can arise because accounting records do not enable identification of intrasector flows and stocks or because errors and omissions are made in the allocation of source and destination codes. Such errors will usually give rise to imbalances that will be detected in the consolidation process. As discussed in chapter 4, every effort is made to resolve such imbalances that are material. When imbalances cannot be resolved in time for publication the data are forced into a balance by adopting a convention (e.g. the record of the ‘higher’ level of government prevails) or making a judgement as to which of the two values should be accepted. Forced balancing does not necessarily give the ‘right’ answer. However, because the data to which forced balancing is applied should not be material, errors arising from this source should not be significant.
6.28. Revisions are amendments made to previously released data. They can occur for a number of reasons. As previously discussed, in GFS a major reason for revisions is the progressive replacement of data over the processing cycle (i.e. the replacement of forward estimates with preliminary estimates, which are in turn replaced by final audited data). Revisions are also required because errors are detected in data after their initial release. Conceptual and methodological changes also give rise to revisions.
6.29. Revisions to GFS data are not applied immediately, but are applied at specified times that coincide with the release of publications. This means that, at any point of time, the data include inaccuracies that will not be corrected until revisions are applied. However, restriction of the application of revisions to particular times is considered preferable to having a data set that is continually subject to change.
6.30. The times of application of revisions to GFS data is currently dictated by the revisions policy for the Australian System of National Accounts. The policy allows revisions to be applied in the releases for various quarters as required by National Accounts Branch.
6.31. Application of the policy is consistent with the release of preliminary GFS estimates for the latest completed financial year in Government Financial Estimates, Australia (ABS Cat. no. 5501.0.55.001) and release of revisions to those data (in the form of final data) in Government Finance Statistics, Australia (ABS Cat. no. 5512.0).